Automated license plate recognition (ALPR) is a mass surveillance method that uses optical character recognition on an image to read a license plate on a vehicle. ALPR is a critical technology for many transportation related solutions such as, for example, red light photo-enforcement and automated tolling systems. ALPR automatically determines the character sequence on the license plate and provides a ticket and/or toll charge with respect to an individual person and/or account. Such transportation solutions typically utilize a digital photograph and/or one or more still shots from a video in order to analyze the license plate of the vehicle.
Most prior art ALPR systems adapt an optical character recognition (OCR) approach to determine the character sequence on the license plate of the automotive vehicle, which could be travelling at high speeds. Such prior art systems perform optical character recognition on the images of the license plate via a supervised learning approach in presence of a number of noise factors. The noise factors associated with the images include for example, various font types and sizes, various jurisdictions, lighting variations, plate mounting variations including plate frame occlusion, weather, plate damage, and plate contamination. In addition to the OCR technique, the prior art ALPR solutions calculate a confidence level on a decoded output in order to ensure accuracy and direct the low confidence images to an operator for manual interpretation.
FIG. 1 illustrates a detailed flow chart of operations illustrating logical operational steps of a prior art method 100 for recognizing images in a license plate of an automotive vehicle based on a supervised learning approach. An image/current result pair with respect to the automotive vehicle can be read, as illustrated at block 110. The parameters associated with the OCR algorithm can be adjusted based on the image/current result pair, as indicated at block 120. A determination can be made whether all the training data sets have been used to adjust the parameters of the OCR algorithm, as depicted at block 130. If all the training data sets have been utilized, the next test license plate image can be read, as illustrated at block 140. Otherwise, the process continues from block 110 to adjust OCR parameters based on training data. Furthermore, the optical character recognition can be performed on the next test license plate image with respect to the set of parameters, as indicated at block 150. A determination can be made whether the confidence level is greater than a lower limit, as illustrated at block 160. If the confidence level is greater than the lower limit, the results can be reported and the process continued from block 140, as depicted at block 180. Otherwise, the human interpretations with respect to the images can be obtained and the process can be continued from block 180, as illustrated at block 170. Here, a test license plate image refers to a license plate image not in the training data set.
An OCR engine associated with the ALPR employs the training data sets, such as a set of sample images and correct interpretations with respect to the automotive vehicle, for identifying the letters and numbers in the images of the license plate. Upon obtaining the initial training data set, the OCR engine can be applied to a larger data set for identifying the images in a real-time application. In such real time applications, the images presented to the OCR engine can include a wider variety of variations than in the training data set due to the noise factors. Alternatively, the breadth of the image variation in the training data set can be increased, which also increases the size of the training data set and the amount of time and cost of training. However, the prior art approaches are unable to provide recognition with high confidence nor calculate a sufficient confidence level with respect to the images based on the training data set with such added image variation. Furthermore, such manual approaches for interpreting the images with respect to the automotive vehicle and the license plate is costly, time consuming and prone to errors.
Based on the foregoing it is believed that a need exists for an improved automated license plate recognition system and method. A need also exists for an improved method for automating license plate recognition utilizing a human-in-the-loop based adaptive learning approach, as described in greater detail herein.